基于多种群粒子群算法和布谷鸟搜索的联合寻优算法
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作者单位:

厦门大学信息技术与科学学院,福建厦门361005.

作者简介:

高云龙

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中图分类号:

TP273

基金项目:

国家自然科学基金项目(61203176);福建省自然科学基金项目(2013J05098).


Unified optimization based on multi-swarm PSO algorithm and cuckoo search algorithm
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School of Information Science and Engineering,Xiamen University,Xiamen 361005,China.

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    摘要:

    为了提高动态多种群粒子群(DMS-PSO) 算法的全局搜索能力, 将布谷鸟搜索算法(CS) 引入DMS-PSO 算法中, 提出DMS-PSO-CS 算法. 采用中位数聚类算法将整个种群动态划分为若干小种群, 各个小种群作为底层种群通过PSO 算法进行寻优, 再将每个小种群中的最优粒子作为高层种群的粒子通过CS 算法进行深度优化. 将所提出算法应用于CEC 2014 测试函数, 并与CS 算法和其他改进的PSO 算法进行比较. 实验结果表明, 所提出算法能够显著提高全局搜索能力和算法效率.

    Abstract:

    In order to improve the global search ability of the dynamic multi-swarm PSO(DMS-PSO) algorithm, the cuckoo search(CS) algorithm is introduced into DMS-PSO algorithm, the algorithm named DMS-PSO-CS is proposed, which is a bi-layer optimal algorithm. The whole swarm is dynamically divided into several small populations by using the median clustering algorithm, and each small population is optimized by using PSO algorithm in the bottom layer. The best particle from each small population is selected as a member of the swarm in the top layer, then the top swarm is deeply optimized by using CS algorithm. The DMS-PSO-CS algorithm is employed to solve the CEC 2014 test functions. In comparision with the CS algorithm and other improved PSO algorithms, the experimental results show that the proposed algorithm can obviously improve the global search ability and computation efficiency.

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高云龙 闫鹏.基于多种群粒子群算法和布谷鸟搜索的联合寻优算法[J].控制与决策,2016,31(4):601-608

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历史
  • 收稿日期:2015-03-23
  • 最后修改日期:2015-06-23
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  • 在线发布日期: 2016-04-20
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